| --- |
| title: BioStack - RLHF Medical Report Generation |
| emoji: 🩻 |
| colorFrom: blue |
| colorTo: indigo |
| sdk: docker |
| app_port: 7860 |
| --- |
| |
| # BioStack - RLHF Medical Report Generation |
|
|
| AI-powered medical report generation using Reinforcement Learning from Human Feedback (RLHF). |
|
|
| ## Features |
| - **SFT Model**: Supervised Fine-Tuning for initial report generation |
| - **Reward Model**: Quality assessment of generated reports |
| - **PPO Model**: Policy optimization for improved outputs |
| - **Modern UI**: Clean React interface with drag-and-drop upload |
|
|
| ## Models Used |
| - **SFT Model**: `best_model.pt` - Initial report generation |
| - **Reward Model**: `reward_model.pt` - Quality scoring |
| - **PPO Model**: `rlhf_model.pt` - Optimized generation |
|
|
| All models are automatically downloaded from Hugging Face Hub on first startup. |
|
|
| ## Usage |
| 1. Upload a medical X-ray image |
| 2. Optionally provide ground truth text for comparison |
| 3. Click "Run Inference" to generate reports |
| 4. View results from all three models with quality scores |
|
|
| ## Technology Stack |
| - **Frontend**: React.js |
| - **Backend**: FastAPI (Python) |
| - **ML Framework**: PyTorch, Transformers |
| - **Models**: T5, CoAtNet, Custom Reward Model |
|
|
| ## Local Development |
|
|
| ### Prerequisites |
| - Python 3.11+ |
| - Node.js 16+ |
| - Hugging Face account with access tokens |
|
|
| ### Installation |
| ```bash |
| # Install Python dependencies |
| pip install -r requirements.txt |
| |
| # Install Node dependencies |
| npm install |
| |
| # Build React app |
| npm run build |
| |
| # Run server |
| python server.py |
| ``` |
|
|
| The app will be available at `http://localhost:7860` |
|
|
| ## Deployment |
| This app is configured for deployment on Hugging Face Spaces using Docker. |
|
|
